@article {70039, title = {MicroSplit: Semantic Unmixing of Fluorescent Microscopy Data}, journal = {bioRxiv}, year = {2025}, month = {2025 Jul 21}, abstract = {

Fluorescence microscopy, a key driver for progress in the life sciences, faces limitations due to the microscope{\textquoteright}s optics, fluorophore chemistry, and photon exposure limits, necessitating trade-offs in imaging speed, resolution, and depth. Here, we introduce MicroSplit, a computational multiplexing technique based on deep learning that allows multiple cellular structures to be imaged in a single fluorescent channel and then unmixed computationally, allowing faster imaging and reduced photon exposure. We show that MicroSplit efficiently separates up to four superimposed noisy structures into distinct denoised fluorescent image channels. Furthermore, using Variational Splitting Encoder-Decoder (VSE) networks, our approach can sample diverse predictions from a trained posterior of solutions. The diversity of these samples scales with the uncertainty in a given input, allowing us to estimate the true prediction errors by computing the variability between posterior samples. We demonstrate the robustness of MicroSplit across various datasets and noise levels and show its utility to image more, image faster, and improve downstream analysis. We provide MicroSplit along with all associated training and evaluation datasets as open resources, enabling life scientists to benefit from the potential of computational multiplexing and accelerate the pace of scientific discovery.

}, doi = {10.1101/2025.02.10.637323}, url = {https://www.biorxiv.org/content/early/2025/07/21/2025.02.10.637323}, author = {Ashesh, Ashesh and Carrara, Federico and Zubarev, Igor and Galinova, Vera and Croft, Melisande and Pezzotti, Melissa and Gong, Daozheng and Casagrande, Francesca and Colombo, Elisa and Giussani, Stefania and Restelli, Elena and Cammarota, Eugenia and Battagliotti, Juan Manuel and Klena, Nikolai and Di Sante, Moises and Adhikari, Raghabendra and Feliciano, Daniel and Pigino, Gaia and Taverna, Elena and Harschnitz, Oliver and Maghelli, Nicola and Scherer, Norbert and Dalle Nogare, Damian Edward and Deschamps, Joran and Pasqualini, Francesco and Jug, Florian} } @article {69226, title = {Live-cell imaging powered by computation.}, journal = {Nature Reviews Molecular Cell Biology}, year = {2024}, month = {2024 Feb 20}, abstract = {

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.

}, issn = {1471-0080}, doi = {10.1038/s41580-024-00702-6}, author = {Shroff, Hari and Testa, Ilaria and Jug, Florian and Manley, Suliana} } @article {65905, title = {3D FIB-SEM reconstruction of microtubule-organelle interaction in whole primary mouse β cells.}, journal = {Journal of Cell Biology}, volume = {220}, year = {2021}, month = {2021 Feb 01}, abstract = {

Microtubules play a major role in intracellular trafficking of vesicles in endocrine cells. Detailed knowledge of microtubule organization and their relation to other cell constituents is crucial for understanding cell function. However, their role in insulin transport and secretion is under debate. Here, we use FIB-SEM to image islet β cells in their entirety with unprecedented resolution. We reconstruct mitochondria, Golgi apparati, centrioles, insulin secretory granules, and microtubules of seven β cells, and generate a comprehensive spatial map of microtubule-organelle interactions. We find that microtubules form nonradial networks that are predominantly not connected to either centrioles or endomembranes. Microtubule number and length, but not microtubule polymer density, vary with glucose stimulation. Furthermore, insulin secretory granules are enriched near the plasma membrane, where they associate with microtubules. In summary, we provide the first 3D reconstructions of complete microtubule networks in primary mammalian cells together with evidence regarding their importance for insulin secretory granule positioning and thus their supportive role in insulin secretion.

}, issn = {1540-8140}, doi = {10.1083/jcb.202010039}, author = {M{\"u}ller, Andreas and Schmidt, Deborah and Xu, C Shan and Pang, Song and D{\textquoteright}Costa, Joyson Verner and Kretschmar, Susanne and M{\"u}nster, Carla and Kurth, Thomas and Jug, Florian and Weigert, Martin and Hess, Harald F and Solimena, Michele} } @article {65111, title = {Regionalized tissue fluidization by an actomyosin cable is required for epithelial gap closure during insect gastrulation.}, journal = {Nature Communications}, volume = {11}, year = {2019}, month = {2020 Aug 22}, pages = {5604}, abstract = {

Many animal embryos face early on in development the problem of having to pull and close an epithelial sheet around the spherical yolk-sac. During this gastrulation process, known as epiboly, the spherical geometry of the egg dictates that the epithelial sheet first expands and subsequently compacts to close around the sphere. While it is well recognized that contractile actomyosin cables can drive epiboly movements, it is unclear how pulling on the leading edge can lead to simultaneous tissue expansion and compaction. Moreover, the epithelial sheet spreading over the sphere is mechanically stressed and this stress needs to be dissipated for seamless closure. While oriented cell division is known to dissipate tissue stresses during epiboly, it is unclear how this can be achieved without cell division. Here we show that during extraembryonic tissue (serosa) epiboly in the red flour beetle Tribolium castaneum, the non-proliferative serosa becomes regionalized into two distinct territories: a dorsal region under higher tension away from the leading edge with larger, isodiametric and non-rearranging cells, and a more fluid ventral region under lower tension surrounding the leading edge with smaller, anisotropic cells undergoing cell intercalation. Our results suggest that fluidization of the leading edge is effected by a heterogeneous actomyosin cable that drives sequential eviction and intercalation of individual cells away from the serosa margin. Since this developmental solution utilized during epiboly resembles the mechanism of wound healing in other systems, we propose actomyosin cable-driven local tissue fluidization as a conserved morphogenetic module for closure of epithelial gaps.

}, doi = {https://doi.org/10.1101/744193}, author = {Jain, Akanksha and Ulman, Vladimir and Mukherjee, Argo and Prakash, Mangal and Pimpale, Lokesh and Munster, Stefan and Panfilio, Kristen A. and Jug, Florian and Grill, Stephan WA. and Tomancak, Pavel and Pavlopoulos, Anastasios} } @article {48163, title = {Automated detection and quantification of single RNAs at cellular resolution in zebrafish embryos.}, journal = {Development (Cambridge, England)}, volume = {143}, year = {2016}, month = {2016 Feb 01}, pages = {540-6}, abstract = {

Analysis of differential gene expression is crucial for the study of cell fate and behavior during embryonic development. However, automated methods for the sensitive detection and quantification of RNAs at cellular resolution in embryos are lacking. With the advent of single-molecule fluorescence in situ hybridization (smFISH), gene expression can be analyzed at single-molecule resolution. However, the limited availability of protocols for smFISH in embryos and the lack of efficient image analysis pipelines have hampered quantification at the (sub)cellular level in complex samples such as tissues and embryos. Here, we present a protocol for smFISH on zebrafish embryo sections in combination with an image analysis pipeline for automated transcript detection and cell segmentation. We use this strategy to quantify gene expression differences between different cell types and identify differences in subcellular transcript localization between genes. The combination of our smFISH protocol and custom-made, freely available, analysis pipeline will enable researchers to fully exploit the benefits of quantitative transcript analysis at cellular and subcellular resolution in tissues and embryos.

}, keywords = {Animals, Automation, Cell Membrane, Embryo, Nonmammalian, Gene Expression Regulation, Developmental, In Situ Hybridization, Fluorescence, RNA, RNA, Messenger, Subcellular Fractions, Transcription, Genetic, Zebrafish}, issn = {1477-9129}, doi = {10.1242/dev.128918}, author = {Stapel, L Carine and Lombardot, Benoit and Broaddus, Coleman and Kainmueller, Dagmar and Jug, Florian and Myers, Eugene W and Vastenhouw, Nadine L} } @article {48164, title = {Active graph matching for automatic joint segmentation and annotation of C. elegans.}, journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention}, volume = {17}, year = {2014}, month = {2014}, pages = {81-8}, abstract = {

In this work we present a novel technique we term active graph matching, which integrates the popular active shape model into a sparse graph matching problem. This way we are able to combine the benefits of a global, statistical deformation model with the benefits of a local deformation model in form of a second-order random field. We present a new iterative energy minimization technique which achieves empirically good results. This enables us to exceed state-of-the art results for the task of annotating nuclei in 3D microscopic images of C. elegans. Furthermore with the help of the generalized Hough transform we are able to jointly segment and annotate a large set of nuclei in a fully automatic fashion for the first time.

}, keywords = {Algorithms, Animals, Caenorhabditis elegans, Cell Nucleus, Image Enhancement, Image Interpretation, Computer-Assisted, Microscopy, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique}, author = {Kainmueller, Dagmar and Jug, Florian and Rother, Carsten and Myers, Gene} } @article {45049, title = {Bioimage informatics in the context of Drosophila research.}, journal = {Methods}, volume = {68}, year = {2014}, month = {2014 Jun 15}, pages = {60-73}, abstract = {

Modern biological research relies heavily on microscopic imaging. The advanced genetic toolkit of Drosophila makes it possible to label molecular and cellular components with unprecedented level of specificity necessitating the application of the most sophisticated imaging technologies. Imaging in Drosophila spans all scales from single molecules to the entire populations of adult organisms, from electron microscopy to live imaging of developmental processes. As the imaging approaches become more complex and ambitious, there is an increasing need for quantitative, computer-mediated image processing and analysis to make sense of the imagery. Bioimage Informatics is an emerging research field that covers all aspects of biological image analysis from data handling, through processing, to quantitative measurements, analysis and data presentation. Some of the most advanced, large scale projects, combining cutting edge imaging with complex bioimage informatics pipelines, are realized in the Drosophila research community. In this review, we discuss the current research in biological image analysis specifically relevant to the type of systems level image datasets that are uniquely available for the Drosophila model system. We focus on how state-of-the-art computer vision algorithms are impacting the ability of Drosophila researchers to analyze biological systems in space and time. We pay particular attention to how these algorithmic advances from computer science are made usable to practicing biologists through open source platforms and how biologists can themselves participate in their further development.

}, issn = {1095-9130}, doi = {10.1016/j.ymeth.2014.04.004}, author = {Jug, Florian and Pietzsch, Tobias and Preibisch, Stephan and Tomancak, Pavel} } @conference {48180, title = {Tracking by assignment facilitates data curation.}, booktitle = {Medical Image Computing and Computer-Assisted Intervention {\textendash} MICCAI Workshop 2014}, year = {2014}, month = {2014 Sep 14}, address = {Boston, MA}, abstract = {

Object tracking is essential for a multitude of biomedical re- search projects. Automated methods are desired in order to avoid im- possible amounts of manual tracking efforts. However, automatically found solutions are not free of errors, and these errors again have to be identified and resolved manually. We propose six innovative ways for semi-automatic curation of automatically found tracking solutions. Respective user interactions are six simple operations: Inclusion and ex- clusion of objects and tracking decisions, specification of the number of objects, and one-click altering of object segmentations. We show how all proposed interactions can be elegantly incorporated into {\textquotedblleft}assignment models{\textquotedblright} [1,2,3,4,5,6], an innovative and increasingly popular tracking paradigm. Given some user interaction, the tracking engine is capable of computing the respective globally optimal tracking solution efficiently, even benefitting from {\textquotedblleft}warm start{\textquotedblright}-capabilities. We show that after in- teractively pointing at a single mistake, multiple segmentation and track- ing errors can be fixed automatically in one single re-evaluation, provably leading to the new, feedback-conscious global optimum.\ 

}, url = {http://interactivemedical.org/imic2014/CameraReadyPapers/Paper\%203/Jug_Interactive_IMIC2014.pdf}, author = {Jug, Florian and Tobias Pietzsch and Kainmueller, Dagmar and Myers, Eugene W} }